Abstract

Shotgun proteomics uses liquid chromatography-tandem mass spectrometry
to identify proteins in complex biological samples. We describe an
algorithm, called Percolator, for improving the rate of peptide
identifications from a collection of tandem mass spectra. Percolator
uses semi-supervised machine learning to discriminate between correct
and decoy spectrum identifications, correctly assigning peptides to
17% more spectra from a tryptic dataset and up to 77% more spectra
from non-tryptic digests, relative to a fully supervised approach.

Download the Percolator software here.
For the experiments reported in the paper, Percolator version 1.01 was
used.